2023-10-13 15:06:11,843 ----------------------------------------------------------------------------------------------------
2023-10-13 15:06:11,844 Model: "SequenceTagger(
  (embeddings): TransformerWordEmbeddings(
    (model): BertModel(
      (embeddings): BertEmbeddings(
        (word_embeddings): Embedding(32001, 768)
        (position_embeddings): Embedding(512, 768)
        (token_type_embeddings): Embedding(2, 768)
        (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
        (dropout): Dropout(p=0.1, inplace=False)
      )
      (encoder): BertEncoder(
        (layer): ModuleList(
          (0-11): 12 x BertLayer(
            (attention): BertAttention(
              (self): BertSelfAttention(
                (query): Linear(in_features=768, out_features=768, bias=True)
                (key): Linear(in_features=768, out_features=768, bias=True)
                (value): Linear(in_features=768, out_features=768, bias=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
              (output): BertSelfOutput(
                (dense): Linear(in_features=768, out_features=768, bias=True)
                (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
                (dropout): Dropout(p=0.1, inplace=False)
              )
            )
            (intermediate): BertIntermediate(
              (dense): Linear(in_features=768, out_features=3072, bias=True)
              (intermediate_act_fn): GELUActivation()
            )
            (output): BertOutput(
              (dense): Linear(in_features=3072, out_features=768, bias=True)
              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
              (dropout): Dropout(p=0.1, inplace=False)
            )
          )
        )
      )
      (pooler): BertPooler(
        (dense): Linear(in_features=768, out_features=768, bias=True)
        (activation): Tanh()
      )
    )
  )
  (locked_dropout): LockedDropout(p=0.5)
  (linear): Linear(in_features=768, out_features=21, bias=True)
  (loss_function): CrossEntropyLoss()
)"
2023-10-13 15:06:11,844 ----------------------------------------------------------------------------------------------------
2023-10-13 15:06:11,844 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences
 - NER_HIPE_2022 Corpus: 5901 train + 1287 dev + 1505 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/fr/with_doc_seperator
2023-10-13 15:06:11,844 ----------------------------------------------------------------------------------------------------
2023-10-13 15:06:11,844 Train:  5901 sentences
2023-10-13 15:06:11,844         (train_with_dev=False, train_with_test=False)
2023-10-13 15:06:11,844 ----------------------------------------------------------------------------------------------------
2023-10-13 15:06:11,845 Training Params:
2023-10-13 15:06:11,845  - learning_rate: "3e-05" 
2023-10-13 15:06:11,845  - mini_batch_size: "8"
2023-10-13 15:06:11,845  - max_epochs: "10"
2023-10-13 15:06:11,845  - shuffle: "True"
2023-10-13 15:06:11,845 ----------------------------------------------------------------------------------------------------
2023-10-13 15:06:11,845 Plugins:
2023-10-13 15:06:11,845  - LinearScheduler | warmup_fraction: '0.1'
2023-10-13 15:06:11,845 ----------------------------------------------------------------------------------------------------
2023-10-13 15:06:11,845 Final evaluation on model from best epoch (best-model.pt)
2023-10-13 15:06:11,845  - metric: "('micro avg', 'f1-score')"
2023-10-13 15:06:11,845 ----------------------------------------------------------------------------------------------------
2023-10-13 15:06:11,845 Computation:
2023-10-13 15:06:11,845  - compute on device: cuda:0
2023-10-13 15:06:11,845  - embedding storage: none
2023-10-13 15:06:11,845 ----------------------------------------------------------------------------------------------------
2023-10-13 15:06:11,845 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
2023-10-13 15:06:11,845 ----------------------------------------------------------------------------------------------------
2023-10-13 15:06:11,845 ----------------------------------------------------------------------------------------------------
2023-10-13 15:06:17,981 epoch 1 - iter 73/738 - loss 3.00595806 - time (sec): 6.13 - samples/sec: 2718.25 - lr: 0.000003 - momentum: 0.000000
2023-10-13 15:06:22,822 epoch 1 - iter 146/738 - loss 2.01431217 - time (sec): 10.98 - samples/sec: 3002.22 - lr: 0.000006 - momentum: 0.000000
2023-10-13 15:06:28,408 epoch 1 - iter 219/738 - loss 1.47513576 - time (sec): 16.56 - samples/sec: 3131.01 - lr: 0.000009 - momentum: 0.000000
2023-10-13 15:06:32,838 epoch 1 - iter 292/738 - loss 1.22557139 - time (sec): 20.99 - samples/sec: 3197.17 - lr: 0.000012 - momentum: 0.000000
2023-10-13 15:06:37,475 epoch 1 - iter 365/738 - loss 1.05610101 - time (sec): 25.63 - samples/sec: 3240.87 - lr: 0.000015 - momentum: 0.000000
2023-10-13 15:06:42,108 epoch 1 - iter 438/738 - loss 0.93629240 - time (sec): 30.26 - samples/sec: 3255.61 - lr: 0.000018 - momentum: 0.000000
2023-10-13 15:06:46,475 epoch 1 - iter 511/738 - loss 0.84807635 - time (sec): 34.63 - samples/sec: 3283.97 - lr: 0.000021 - momentum: 0.000000
2023-10-13 15:06:51,169 epoch 1 - iter 584/738 - loss 0.77373600 - time (sec): 39.32 - samples/sec: 3278.82 - lr: 0.000024 - momentum: 0.000000
2023-10-13 15:06:56,762 epoch 1 - iter 657/738 - loss 0.69819945 - time (sec): 44.92 - samples/sec: 3299.98 - lr: 0.000027 - momentum: 0.000000
2023-10-13 15:07:01,779 epoch 1 - iter 730/738 - loss 0.64790807 - time (sec): 49.93 - samples/sec: 3302.06 - lr: 0.000030 - momentum: 0.000000
2023-10-13 15:07:02,220 ----------------------------------------------------------------------------------------------------
2023-10-13 15:07:02,220 EPOCH 1 done: loss 0.6428 - lr: 0.000030
2023-10-13 15:07:08,107 DEV : loss 0.14631909132003784 - f1-score (micro avg)  0.7001
2023-10-13 15:07:08,134 saving best model
2023-10-13 15:07:08,525 ----------------------------------------------------------------------------------------------------
2023-10-13 15:07:14,091 epoch 2 - iter 73/738 - loss 0.16706915 - time (sec): 5.56 - samples/sec: 3041.31 - lr: 0.000030 - momentum: 0.000000
2023-10-13 15:07:18,341 epoch 2 - iter 146/738 - loss 0.14761739 - time (sec): 9.81 - samples/sec: 3167.97 - lr: 0.000029 - momentum: 0.000000
2023-10-13 15:07:23,052 epoch 2 - iter 219/738 - loss 0.14448689 - time (sec): 14.53 - samples/sec: 3195.29 - lr: 0.000029 - momentum: 0.000000
2023-10-13 15:07:27,919 epoch 2 - iter 292/738 - loss 0.14158495 - time (sec): 19.39 - samples/sec: 3231.27 - lr: 0.000029 - momentum: 0.000000
2023-10-13 15:07:32,228 epoch 2 - iter 365/738 - loss 0.13700805 - time (sec): 23.70 - samples/sec: 3290.03 - lr: 0.000028 - momentum: 0.000000
2023-10-13 15:07:38,405 epoch 2 - iter 438/738 - loss 0.13803403 - time (sec): 29.88 - samples/sec: 3339.44 - lr: 0.000028 - momentum: 0.000000
2023-10-13 15:07:43,280 epoch 2 - iter 511/738 - loss 0.13381098 - time (sec): 34.75 - samples/sec: 3339.59 - lr: 0.000028 - momentum: 0.000000
2023-10-13 15:07:48,287 epoch 2 - iter 584/738 - loss 0.13192888 - time (sec): 39.76 - samples/sec: 3331.69 - lr: 0.000027 - momentum: 0.000000
2023-10-13 15:07:53,037 epoch 2 - iter 657/738 - loss 0.12867654 - time (sec): 44.51 - samples/sec: 3348.07 - lr: 0.000027 - momentum: 0.000000
2023-10-13 15:07:57,470 epoch 2 - iter 730/738 - loss 0.12546326 - time (sec): 48.94 - samples/sec: 3365.74 - lr: 0.000027 - momentum: 0.000000
2023-10-13 15:07:57,943 ----------------------------------------------------------------------------------------------------
2023-10-13 15:07:57,943 EPOCH 2 done: loss 0.1252 - lr: 0.000027
2023-10-13 15:08:08,895 DEV : loss 0.10444298386573792 - f1-score (micro avg)  0.7812
2023-10-13 15:08:08,935 saving best model
2023-10-13 15:08:09,490 ----------------------------------------------------------------------------------------------------
2023-10-13 15:08:14,272 epoch 3 - iter 73/738 - loss 0.07135036 - time (sec): 4.78 - samples/sec: 3180.23 - lr: 0.000026 - momentum: 0.000000
2023-10-13 15:08:18,952 epoch 3 - iter 146/738 - loss 0.06016871 - time (sec): 9.46 - samples/sec: 3299.03 - lr: 0.000026 - momentum: 0.000000
2023-10-13 15:08:23,756 epoch 3 - iter 219/738 - loss 0.06731171 - time (sec): 14.26 - samples/sec: 3388.02 - lr: 0.000026 - momentum: 0.000000
2023-10-13 15:08:28,852 epoch 3 - iter 292/738 - loss 0.06774648 - time (sec): 19.36 - samples/sec: 3379.36 - lr: 0.000025 - momentum: 0.000000
2023-10-13 15:08:33,951 epoch 3 - iter 365/738 - loss 0.07186902 - time (sec): 24.46 - samples/sec: 3390.49 - lr: 0.000025 - momentum: 0.000000
2023-10-13 15:08:38,497 epoch 3 - iter 438/738 - loss 0.07329844 - time (sec): 29.00 - samples/sec: 3374.13 - lr: 0.000025 - momentum: 0.000000
2023-10-13 15:08:43,281 epoch 3 - iter 511/738 - loss 0.07169878 - time (sec): 33.79 - samples/sec: 3383.78 - lr: 0.000024 - momentum: 0.000000
2023-10-13 15:08:48,666 epoch 3 - iter 584/738 - loss 0.07135414 - time (sec): 39.17 - samples/sec: 3352.61 - lr: 0.000024 - momentum: 0.000000
2023-10-13 15:08:53,320 epoch 3 - iter 657/738 - loss 0.07090972 - time (sec): 43.83 - samples/sec: 3371.01 - lr: 0.000024 - momentum: 0.000000
2023-10-13 15:08:58,624 epoch 3 - iter 730/738 - loss 0.07063718 - time (sec): 49.13 - samples/sec: 3355.13 - lr: 0.000023 - momentum: 0.000000
2023-10-13 15:08:59,067 ----------------------------------------------------------------------------------------------------
2023-10-13 15:08:59,067 EPOCH 3 done: loss 0.0703 - lr: 0.000023
2023-10-13 15:09:10,781 DEV : loss 0.11466138809919357 - f1-score (micro avg)  0.7901
2023-10-13 15:09:10,815 saving best model
2023-10-13 15:09:11,367 ----------------------------------------------------------------------------------------------------
2023-10-13 15:09:16,072 epoch 4 - iter 73/738 - loss 0.04052283 - time (sec): 4.70 - samples/sec: 3351.70 - lr: 0.000023 - momentum: 0.000000
2023-10-13 15:09:21,227 epoch 4 - iter 146/738 - loss 0.04104392 - time (sec): 9.86 - samples/sec: 3391.42 - lr: 0.000023 - momentum: 0.000000
2023-10-13 15:09:26,698 epoch 4 - iter 219/738 - loss 0.04349424 - time (sec): 15.33 - samples/sec: 3397.14 - lr: 0.000022 - momentum: 0.000000
2023-10-13 15:09:31,305 epoch 4 - iter 292/738 - loss 0.04220332 - time (sec): 19.93 - samples/sec: 3382.12 - lr: 0.000022 - momentum: 0.000000
2023-10-13 15:09:35,929 epoch 4 - iter 365/738 - loss 0.04287845 - time (sec): 24.56 - samples/sec: 3378.03 - lr: 0.000022 - momentum: 0.000000
2023-10-13 15:09:40,356 epoch 4 - iter 438/738 - loss 0.04241677 - time (sec): 28.98 - samples/sec: 3364.05 - lr: 0.000021 - momentum: 0.000000
2023-10-13 15:09:45,522 epoch 4 - iter 511/738 - loss 0.04273484 - time (sec): 34.15 - samples/sec: 3370.50 - lr: 0.000021 - momentum: 0.000000
2023-10-13 15:09:50,179 epoch 4 - iter 584/738 - loss 0.04336454 - time (sec): 38.81 - samples/sec: 3360.30 - lr: 0.000021 - momentum: 0.000000
2023-10-13 15:09:55,539 epoch 4 - iter 657/738 - loss 0.04399407 - time (sec): 44.17 - samples/sec: 3357.73 - lr: 0.000020 - momentum: 0.000000
2023-10-13 15:10:00,301 epoch 4 - iter 730/738 - loss 0.04575017 - time (sec): 48.93 - samples/sec: 3370.96 - lr: 0.000020 - momentum: 0.000000
2023-10-13 15:10:00,739 ----------------------------------------------------------------------------------------------------
2023-10-13 15:10:00,740 EPOCH 4 done: loss 0.0455 - lr: 0.000020
2023-10-13 15:10:12,198 DEV : loss 0.1425015777349472 - f1-score (micro avg)  0.8148
2023-10-13 15:10:12,235 saving best model
2023-10-13 15:10:12,842 ----------------------------------------------------------------------------------------------------
2023-10-13 15:10:17,689 epoch 5 - iter 73/738 - loss 0.03811823 - time (sec): 4.84 - samples/sec: 3173.32 - lr: 0.000020 - momentum: 0.000000
2023-10-13 15:10:22,809 epoch 5 - iter 146/738 - loss 0.03085581 - time (sec): 9.96 - samples/sec: 3137.72 - lr: 0.000019 - momentum: 0.000000
2023-10-13 15:10:27,945 epoch 5 - iter 219/738 - loss 0.03743163 - time (sec): 15.10 - samples/sec: 3218.04 - lr: 0.000019 - momentum: 0.000000
2023-10-13 15:10:32,662 epoch 5 - iter 292/738 - loss 0.03362175 - time (sec): 19.82 - samples/sec: 3227.76 - lr: 0.000019 - momentum: 0.000000
2023-10-13 15:10:37,919 epoch 5 - iter 365/738 - loss 0.03326265 - time (sec): 25.07 - samples/sec: 3248.42 - lr: 0.000018 - momentum: 0.000000
2023-10-13 15:10:43,093 epoch 5 - iter 438/738 - loss 0.03370392 - time (sec): 30.25 - samples/sec: 3253.05 - lr: 0.000018 - momentum: 0.000000
2023-10-13 15:10:47,701 epoch 5 - iter 511/738 - loss 0.03447331 - time (sec): 34.85 - samples/sec: 3266.99 - lr: 0.000018 - momentum: 0.000000
2023-10-13 15:10:52,511 epoch 5 - iter 584/738 - loss 0.03383997 - time (sec): 39.66 - samples/sec: 3275.72 - lr: 0.000017 - momentum: 0.000000
2023-10-13 15:10:57,968 epoch 5 - iter 657/738 - loss 0.03331943 - time (sec): 45.12 - samples/sec: 3288.68 - lr: 0.000017 - momentum: 0.000000
2023-10-13 15:11:02,727 epoch 5 - iter 730/738 - loss 0.03414638 - time (sec): 49.88 - samples/sec: 3307.94 - lr: 0.000017 - momentum: 0.000000
2023-10-13 15:11:03,167 ----------------------------------------------------------------------------------------------------
2023-10-13 15:11:03,167 EPOCH 5 done: loss 0.0341 - lr: 0.000017
2023-10-13 15:11:14,225 DEV : loss 0.16617898643016815 - f1-score (micro avg)  0.8201
2023-10-13 15:11:14,254 saving best model
2023-10-13 15:11:14,766 ----------------------------------------------------------------------------------------------------
2023-10-13 15:11:19,314 epoch 6 - iter 73/738 - loss 0.03653394 - time (sec): 4.54 - samples/sec: 3289.33 - lr: 0.000016 - momentum: 0.000000
2023-10-13 15:11:24,335 epoch 6 - iter 146/738 - loss 0.02834807 - time (sec): 9.56 - samples/sec: 3172.23 - lr: 0.000016 - momentum: 0.000000
2023-10-13 15:11:29,927 epoch 6 - iter 219/738 - loss 0.02694544 - time (sec): 15.15 - samples/sec: 3243.68 - lr: 0.000016 - momentum: 0.000000
2023-10-13 15:11:34,905 epoch 6 - iter 292/738 - loss 0.02900938 - time (sec): 20.13 - samples/sec: 3232.05 - lr: 0.000015 - momentum: 0.000000
2023-10-13 15:11:39,564 epoch 6 - iter 365/738 - loss 0.02925216 - time (sec): 24.79 - samples/sec: 3266.97 - lr: 0.000015 - momentum: 0.000000
2023-10-13 15:11:44,927 epoch 6 - iter 438/738 - loss 0.02862355 - time (sec): 30.15 - samples/sec: 3284.34 - lr: 0.000015 - momentum: 0.000000
2023-10-13 15:11:49,476 epoch 6 - iter 511/738 - loss 0.02801253 - time (sec): 34.70 - samples/sec: 3289.74 - lr: 0.000014 - momentum: 0.000000
2023-10-13 15:11:54,293 epoch 6 - iter 584/738 - loss 0.02697259 - time (sec): 39.52 - samples/sec: 3292.93 - lr: 0.000014 - momentum: 0.000000
2023-10-13 15:11:59,783 epoch 6 - iter 657/738 - loss 0.02638200 - time (sec): 45.01 - samples/sec: 3312.23 - lr: 0.000014 - momentum: 0.000000
2023-10-13 15:12:04,543 epoch 6 - iter 730/738 - loss 0.02702535 - time (sec): 49.77 - samples/sec: 3311.87 - lr: 0.000013 - momentum: 0.000000
2023-10-13 15:12:05,010 ----------------------------------------------------------------------------------------------------
2023-10-13 15:12:05,011 EPOCH 6 done: loss 0.0271 - lr: 0.000013
2023-10-13 15:12:16,088 DEV : loss 0.17802561819553375 - f1-score (micro avg)  0.8279
2023-10-13 15:12:16,116 saving best model
2023-10-13 15:12:16,626 ----------------------------------------------------------------------------------------------------
2023-10-13 15:12:21,328 epoch 7 - iter 73/738 - loss 0.02201187 - time (sec): 4.70 - samples/sec: 3237.82 - lr: 0.000013 - momentum: 0.000000
2023-10-13 15:12:26,848 epoch 7 - iter 146/738 - loss 0.01959250 - time (sec): 10.22 - samples/sec: 3292.24 - lr: 0.000013 - momentum: 0.000000
2023-10-13 15:12:31,310 epoch 7 - iter 219/738 - loss 0.01698476 - time (sec): 14.68 - samples/sec: 3318.25 - lr: 0.000012 - momentum: 0.000000
2023-10-13 15:12:36,512 epoch 7 - iter 292/738 - loss 0.01780977 - time (sec): 19.88 - samples/sec: 3258.64 - lr: 0.000012 - momentum: 0.000000
2023-10-13 15:12:41,639 epoch 7 - iter 365/738 - loss 0.01729180 - time (sec): 25.01 - samples/sec: 3270.72 - lr: 0.000012 - momentum: 0.000000
2023-10-13 15:12:46,745 epoch 7 - iter 438/738 - loss 0.01848562 - time (sec): 30.11 - samples/sec: 3315.54 - lr: 0.000011 - momentum: 0.000000
2023-10-13 15:12:52,092 epoch 7 - iter 511/738 - loss 0.01773880 - time (sec): 35.46 - samples/sec: 3309.93 - lr: 0.000011 - momentum: 0.000000
2023-10-13 15:12:57,414 epoch 7 - iter 584/738 - loss 0.01697287 - time (sec): 40.78 - samples/sec: 3290.74 - lr: 0.000011 - momentum: 0.000000
2023-10-13 15:13:02,062 epoch 7 - iter 657/738 - loss 0.01729116 - time (sec): 45.43 - samples/sec: 3278.94 - lr: 0.000010 - momentum: 0.000000
2023-10-13 15:13:06,934 epoch 7 - iter 730/738 - loss 0.01695416 - time (sec): 50.30 - samples/sec: 3271.06 - lr: 0.000010 - momentum: 0.000000
2023-10-13 15:13:07,414 ----------------------------------------------------------------------------------------------------
2023-10-13 15:13:07,414 EPOCH 7 done: loss 0.0171 - lr: 0.000010
2023-10-13 15:13:18,417 DEV : loss 0.1945790946483612 - f1-score (micro avg)  0.8243
2023-10-13 15:13:18,444 ----------------------------------------------------------------------------------------------------
2023-10-13 15:13:23,184 epoch 8 - iter 73/738 - loss 0.01028889 - time (sec): 4.74 - samples/sec: 3410.19 - lr: 0.000010 - momentum: 0.000000
2023-10-13 15:13:27,830 epoch 8 - iter 146/738 - loss 0.01125169 - time (sec): 9.38 - samples/sec: 3402.58 - lr: 0.000009 - momentum: 0.000000
2023-10-13 15:13:32,888 epoch 8 - iter 219/738 - loss 0.01185173 - time (sec): 14.44 - samples/sec: 3435.18 - lr: 0.000009 - momentum: 0.000000
2023-10-13 15:13:37,825 epoch 8 - iter 292/738 - loss 0.01232791 - time (sec): 19.38 - samples/sec: 3373.16 - lr: 0.000009 - momentum: 0.000000
2023-10-13 15:13:42,461 epoch 8 - iter 365/738 - loss 0.01366262 - time (sec): 24.02 - samples/sec: 3372.28 - lr: 0.000008 - momentum: 0.000000
2023-10-13 15:13:47,566 epoch 8 - iter 438/738 - loss 0.01654620 - time (sec): 29.12 - samples/sec: 3339.24 - lr: 0.000008 - momentum: 0.000000
2023-10-13 15:13:52,187 epoch 8 - iter 511/738 - loss 0.01569334 - time (sec): 33.74 - samples/sec: 3335.61 - lr: 0.000008 - momentum: 0.000000
2023-10-13 15:13:57,753 epoch 8 - iter 584/738 - loss 0.01574651 - time (sec): 39.31 - samples/sec: 3330.91 - lr: 0.000007 - momentum: 0.000000
2023-10-13 15:14:02,442 epoch 8 - iter 657/738 - loss 0.01505006 - time (sec): 44.00 - samples/sec: 3337.63 - lr: 0.000007 - momentum: 0.000000
2023-10-13 15:14:07,726 epoch 8 - iter 730/738 - loss 0.01414673 - time (sec): 49.28 - samples/sec: 3345.74 - lr: 0.000007 - momentum: 0.000000
2023-10-13 15:14:08,175 ----------------------------------------------------------------------------------------------------
2023-10-13 15:14:08,175 EPOCH 8 done: loss 0.0141 - lr: 0.000007
2023-10-13 15:14:19,189 DEV : loss 0.208764910697937 - f1-score (micro avg)  0.8257
2023-10-13 15:14:19,216 ----------------------------------------------------------------------------------------------------
2023-10-13 15:14:24,185 epoch 9 - iter 73/738 - loss 0.01390651 - time (sec): 4.97 - samples/sec: 3487.39 - lr: 0.000006 - momentum: 0.000000
2023-10-13 15:14:28,985 epoch 9 - iter 146/738 - loss 0.01196049 - time (sec): 9.77 - samples/sec: 3443.85 - lr: 0.000006 - momentum: 0.000000
2023-10-13 15:14:33,824 epoch 9 - iter 219/738 - loss 0.01250429 - time (sec): 14.61 - samples/sec: 3380.42 - lr: 0.000006 - momentum: 0.000000
2023-10-13 15:14:38,723 epoch 9 - iter 292/738 - loss 0.01082105 - time (sec): 19.51 - samples/sec: 3355.41 - lr: 0.000005 - momentum: 0.000000
2023-10-13 15:14:43,628 epoch 9 - iter 365/738 - loss 0.01081319 - time (sec): 24.41 - samples/sec: 3341.46 - lr: 0.000005 - momentum: 0.000000
2023-10-13 15:14:48,215 epoch 9 - iter 438/738 - loss 0.01107780 - time (sec): 29.00 - samples/sec: 3343.77 - lr: 0.000005 - momentum: 0.000000
2023-10-13 15:14:53,022 epoch 9 - iter 511/738 - loss 0.01096276 - time (sec): 33.80 - samples/sec: 3377.28 - lr: 0.000004 - momentum: 0.000000
2023-10-13 15:14:58,322 epoch 9 - iter 584/738 - loss 0.01113887 - time (sec): 39.11 - samples/sec: 3358.91 - lr: 0.000004 - momentum: 0.000000
2023-10-13 15:15:03,199 epoch 9 - iter 657/738 - loss 0.01099575 - time (sec): 43.98 - samples/sec: 3363.25 - lr: 0.000004 - momentum: 0.000000
2023-10-13 15:15:07,995 epoch 9 - iter 730/738 - loss 0.01087907 - time (sec): 48.78 - samples/sec: 3369.09 - lr: 0.000003 - momentum: 0.000000
2023-10-13 15:15:08,627 ----------------------------------------------------------------------------------------------------
2023-10-13 15:15:08,627 EPOCH 9 done: loss 0.0107 - lr: 0.000003
2023-10-13 15:15:19,652 DEV : loss 0.21101868152618408 - f1-score (micro avg)  0.83
2023-10-13 15:15:19,682 saving best model
2023-10-13 15:15:20,194 ----------------------------------------------------------------------------------------------------
2023-10-13 15:15:25,141 epoch 10 - iter 73/738 - loss 0.00803248 - time (sec): 4.94 - samples/sec: 3261.82 - lr: 0.000003 - momentum: 0.000000
2023-10-13 15:15:31,005 epoch 10 - iter 146/738 - loss 0.00805600 - time (sec): 10.81 - samples/sec: 3277.85 - lr: 0.000003 - momentum: 0.000000
2023-10-13 15:15:36,594 epoch 10 - iter 219/738 - loss 0.00898068 - time (sec): 16.40 - samples/sec: 3134.89 - lr: 0.000002 - momentum: 0.000000
2023-10-13 15:15:41,054 epoch 10 - iter 292/738 - loss 0.00813472 - time (sec): 20.86 - samples/sec: 3202.98 - lr: 0.000002 - momentum: 0.000000
2023-10-13 15:15:45,471 epoch 10 - iter 365/738 - loss 0.00754629 - time (sec): 25.27 - samples/sec: 3253.82 - lr: 0.000002 - momentum: 0.000000
2023-10-13 15:15:49,965 epoch 10 - iter 438/738 - loss 0.00782211 - time (sec): 29.77 - samples/sec: 3272.23 - lr: 0.000001 - momentum: 0.000000
2023-10-13 15:15:55,145 epoch 10 - iter 511/738 - loss 0.00825368 - time (sec): 34.95 - samples/sec: 3287.64 - lr: 0.000001 - momentum: 0.000000
2023-10-13 15:16:00,172 epoch 10 - iter 584/738 - loss 0.00879952 - time (sec): 39.97 - samples/sec: 3277.10 - lr: 0.000001 - momentum: 0.000000
2023-10-13 15:16:05,095 epoch 10 - iter 657/738 - loss 0.00821806 - time (sec): 44.90 - samples/sec: 3273.46 - lr: 0.000000 - momentum: 0.000000
2023-10-13 15:16:10,579 epoch 10 - iter 730/738 - loss 0.00815587 - time (sec): 50.38 - samples/sec: 3275.14 - lr: 0.000000 - momentum: 0.000000
2023-10-13 15:16:11,019 ----------------------------------------------------------------------------------------------------
2023-10-13 15:16:11,019 EPOCH 10 done: loss 0.0081 - lr: 0.000000
2023-10-13 15:16:22,129 DEV : loss 0.21044372022151947 - f1-score (micro avg)  0.8237
2023-10-13 15:16:22,555 ----------------------------------------------------------------------------------------------------
2023-10-13 15:16:22,556 Loading model from best epoch ...
2023-10-13 15:16:24,438 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-time, B-time, E-time, I-time, S-prod, B-prod, E-prod, I-prod
2023-10-13 15:16:30,319 
Results:
- F-score (micro) 0.7963
- F-score (macro) 0.6911
- Accuracy 0.6835

By class:
              precision    recall  f1-score   support

         loc     0.8486    0.8951    0.8712       858
        pers     0.7409    0.7933    0.7662       537
         org     0.5840    0.5530    0.5681       132
        time     0.5231    0.6296    0.5714        54
        prod     0.7451    0.6230    0.6786        61

   micro avg     0.7780    0.8155    0.7963      1642
   macro avg     0.6883    0.6988    0.6911      1642
weighted avg     0.7776    0.8155    0.7955      1642

2023-10-13 15:16:30,319 ----------------------------------------------------------------------------------------------------